Convolutional Motif Kernel Networks
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Artificial neural networks are exceptionally good in learning to detect correlations within data that are associated with specified outcomes. However to deepen knowledge and support further research, researchers have to be able to explain predicted outcomes within the data's domain. Furthermore, domain experts like Healthcare Providers need these explanations to assess whether a predicted outcome can be trusted in high stakes scenarios and to help them incorporating a model into their own routine. In this paper we introduce Convolutional Motif Kernel Networks, a neural network architecture that incorporates learning a feature representation within a subspace of the reproducing kernel Hilbert space of the motif kernel function. The resulting model has state-of-the-art performance and enables researchers and domain experts to directly interpret and verify prediction outcomes without the need for a post hoc explainability method.
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